13,585 research outputs found
Computational Simulation and 3D Virtual Reality Engineering Tools for Dynamical Modeling and Imaging of Composite Nanomaterials
An adventure at engineering design and modeling is possible with a Virtual
Reality Environment (VRE) that uses multiple computer-generated media to let a
user experience situations that are temporally and spatially prohibiting. In
this paper, an approach to developing some advanced architecture and modeling
tools is presented to allow multiple frameworks work together while being
shielded from the application program. This architecture is being developed in
a framework of workbench interactive tools for next generation
nanoparticle-reinforced damping/dynamic systems. Through the use of system, an
engineer/programmer can respectively concentrate on tailoring an engineering
design concept of novel system and the application software design while using
existing databases/software outputs.Comment: Submitted on behalf of TIMA Editions
(http://irevues.inist.fr/tima-editions
Filamentary Switching: Synaptic Plasticity through Device Volatility
Replicating the computational functionalities and performances of the brain
remains one of the biggest challenges for the future of information and
communication technologies. Such an ambitious goal requires research efforts
from the architecture level to the basic device level (i.e., investigating the
opportunities offered by emerging nanotechnologies to build such systems).
Nanodevices, or, more precisely, memory or memristive devices, have been
proposed for the implementation of synaptic functions, offering the required
features and integration in a single component. In this paper, we demonstrate
that the basic physics involved in the filamentary switching of electrochemical
metallization cells can reproduce important biological synaptic functions that
are key mechanisms for information processing and storage. The transition from
short- to long-term plasticity has been reported as a direct consequence of
filament growth (i.e., increased conductance) in filamentary memory devices. In
this paper, we show that a more complex filament shape, such as dendritic paths
of variable density and width, can permit the short- and long-term processes to
be controlled independently. Our solid-state device is strongly analogous to
biological synapses, as indicated by the interpretation of the results from the
framework of a phenomenological model developed for biological synapses. We
describe a single memristive element containing a rich panel of features, which
will be of benefit to future neuromorphic hardware systems
Computing parametrized solutions for plasmonic nanogap structures
The interaction of electromagnetic waves with metallic nanostructures
generates resonant oscillations of the conduction-band electrons at the metal
surface. These resonances can lead to large enhancements of the incident field
and to the confinement of light to small regions, typically several orders of
magnitude smaller than the incident wavelength. The accurate prediction of
these resonances entails several challenges. Small geometric variations in the
plasmonic structure may lead to large variations in the electromagnetic field
responses. Furthermore, the material parameters that characterize the optical
behavior of metals at the nanoscale need to be determined experimentally and
are consequently subject to measurement errors. It then becomes essential that
any predictive tool for the simulation and design of plasmonic structures
accounts for fabrication tolerances and measurement uncertainties.
In this paper, we develop a reduced order modeling framework that is capable
of real-time accurate electromagnetic responses of plasmonic nanogap structures
for a wide range of geometry and material parameters. The main ingredients of
the proposed method are: (i) the hybridizable discontinuous Galerkin method to
numerically solve the equations governing electromagnetic wave propagation in
dielectric and metallic media, (ii) a reference domain formulation of the
time-harmonic Maxwell's equations to account for geometry variations; and (iii)
proper orthogonal decomposition and empirical interpolation techniques to
construct an efficient reduced model. To demonstrate effectiveness of the
models developed, we analyze geometry sensitivities and explore optimal designs
of a 3D periodic annular nanogap structure.Comment: 28 pages, 9 figures, 4 tables, 2 appendice
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